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Using Natural Language to Improve the Generation of Model Transformation in Software Design

Jean-Rémy Falleri 1 Violaine Prince 2 Mathieu Lafourcade 2 Michel Dao 3 Marianne Huchard 1 Clémentine Nebut 1
1 MAREL - Models And Reuse Engineering, Languages
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 TEXTE - Exploration et exploitation de données textuelles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Among the present crucial issues in UML Modeling, one of the most common is about the fusion of similar models coming from various sources. Several similar models are created in Software Engineering and it is of primary interest to compare them and, when possible, to craft a general model including a specific one, or just identify models that are in fact equivalent. Most present approaches are based on model structure comparison and alignment on strings for attributes and classe names. This contribution evaluates the added value of several combined NLP techniques based on lexical networks, POS tagging, and Dependency Rules application, and how they might improve the fusion of models. Topics: use of NLP techniques in practical applications.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00421888
Contributor : Violaine Prince-Barbier <>
Submitted on : Monday, October 5, 2009 - 10:18:47 AM
Last modification on : Friday, February 15, 2019 - 3:22:03 PM

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  • HAL Id : lirmm-00421888, version 1

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Jean-Rémy Falleri, Violaine Prince, Mathieu Lafourcade, Michel Dao, Marianne Huchard, et al.. Using Natural Language to Improve the Generation of Model Transformation in Software Design. Computational Linguistics Applications - International Multi-Conference on Computer Science and Information Technology, Oct 2009, Mrogwo, Poland. pp.143-150. ⟨lirmm-00421888⟩

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